An integrative hetnet (heterogeneous network) encoding biomedical knowledge for drug repurposing and discovery.
Hetionet is a heterogeneous network (hetnet) that integrates multiple types of biomedical entities and their relationships into a single, structured knowledge graph. It connects genes, diseases, drugs, pathways, and other biological concepts to enable computational exploration and hypothesis generation, specifically for drug repurposing and understanding therapeutic mechanisms.
Bioinformaticians, computational biologists, and researchers in drug discovery who need a pre-integrated, large-scale knowledge graph for network analysis, machine learning, or hypothesis testing in biomedical domains.
It provides a unique, publicly available integrative network that combines diverse data sources with clear metadata and multiple export formats, facilitating reproducible research without the need for manual data integration.
Hetionet: an integrative network of disease
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Encodes 11 node types and 24 relationship types, enabling rich biological modeling as visualized in the metagraph, which supports complex queries across genes, diseases, and drugs.
Available in JSON, Neo4j, TSV, and matrix archives, allowing integration into various tools; Neo4j is recommended for its ecosystem, while JSON works with the hetio Python package.
Includes per-node and per-edge licensing info and definitions in describe/definitions.json, ensuring proper attribution and understanding of integrated biomedical sources.
Provides permuted versions for null hypothesis testing and BEL conversion for standardized representation, enhancing statistical validation and interoperability in research.
Version 1.0 is a snapshot with infrequent updates; data integration occurs in a separate repository, requiring manual downloads for new versions without automatic updates.
Cloning requires Git LFS for large files, and the JSON format is new and tied to the hetio package, adding steps for local access and parsing.
Integrates data from many sources with mixed licenses (CC0 to proprietary), complicating compliance for users, especially in commercial or redistributive scenarios.